{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AE outlier detection on CIFAR10\n",
    "\n",
    "### Method\n",
    "\n",
    "The Auto-Encoder (AE) outlier detector is first trained on a batch of unlabeled, but normal (inlier) data. Unsupervised training is desireable since labeled data is often scarce. The AE detector tries to reconstruct the input it receives. If the input data cannot be reconstructed well, the reconstruction error is high and the data can be flagged as an outlier. The reconstruction error is  measured as the mean squared error (MSE) between the input and the reconstructed instance.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) consists of 60,000 32 by 32 RGB images equally distributed over 10 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:fbprophet:Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "tf.keras.backend.clear_session()\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, \\\n",
    "    Dense, Layer, Reshape, InputLayer, Flatten\n",
    "from tqdm import tqdm\n",
    "\n",
    "from alibi_detect.od import OutlierAE\n",
    "from alibi_detect.utils.fetching import fetch_detector\n",
    "from alibi_detect.utils.perturbation import apply_mask\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.utils.visualize import plot_instance_score, plot_feature_outlier_image\n",
    "\n",
    "logger = tf.get_logger()\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load CIFAR10 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "train, test = tf.keras.datasets.cifar10.load_data()\n",
    "X_train, y_train = train\n",
    "X_test, y_test = test\n",
    "\n",
    "X_train = X_train.astype('float32') / 255\n",
    "X_test = X_test.astype('float32') / 255\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load or define outlier detector\n",
    "\n",
    "The pretrained outlier and adversarial detectors used in the example notebooks can be found [here](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect). You can use the built-in ```fetch_detector``` function which saves the pre-trained models in a local directory ```filepath``` and loads the detector. Alternatively, you can train a detector from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_outlier_detector = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "filepath = 'my_path'  # change to (absolute) directory where model is downloaded\n",
    "detector_type = 'outlier'\n",
    "dataset = 'cifar10'\n",
    "detector_name = 'OutlierAE'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_outlier_detector:  # load pretrained outlier detector\n",
    "    od = fetch_detector(filepath, detector_type, dataset, detector_name)\n",
    "else:  # define model, initialize, train and save outlier detector\n",
    "    encoding_dim = 1024\n",
    "    \n",
    "    encoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(32, 32, 3)),\n",
    "          Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Flatten(),\n",
    "          Dense(encoding_dim,)\n",
    "      ])\n",
    "\n",
    "    decoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(encoding_dim,)),\n",
    "          Dense(4*4*128),\n",
    "          Reshape(target_shape=(4, 4, 128)),\n",
    "          Conv2DTranspose(256, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(3, 4, strides=2, padding='same', activation='sigmoid')\n",
    "      ])\n",
    "    \n",
    "    # initialize outlier detector\n",
    "    od = OutlierAE(threshold=.015,  # threshold for outlier score\n",
    "                    encoder_net=encoder_net,  # can also pass AE model instead\n",
    "                    decoder_net=decoder_net,  # of separate encoder and decoder\n",
    "                    )\n",
    "    # train\n",
    "    od.fit(X_train,\n",
    "           epochs=50,\n",
    "           verbose=True)\n",
    "    \n",
    "    # save the trained outlier detector\n",
    "    save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check quality AE model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 8\n",
    "X = X_train[idx].reshape(1, 32, 32, 3)\n",
    "X_recon = od.ae(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qx5a1zbLW1Snq1ZV8R+nFlTV9HncfyNjVT7W98d5/6W7NV2/lJ7enlNL+SFx3R9seP/ijl2TsL17UnbkXzKd4QbR4uH1f20C37ut7tXeg20L895W8xZVSSh+9dzm73bVj6J3RCzQaZx8Nt2QsuUn9wtrzVgoT3wEeGxAnQFAQJ0BQECdAUBAnQFAQJ0BQrJXyizu6FP94pEvx3/2fvJUy0GVg0pZZxfDJPZ3Of2JlWcb++s3vZLe/9I3X5JjeYt5+SSmlY2eelrGTf3BRxr4vVnyklNLJzbyts75ouoAv6hvZX9B1cZZMrCtqJx2M9XPeGupVKXd3tNX2sxN6FclRk19ldOehtrFa09p6uKVtrNqU/Fkc6PeqLfM2i7NS2ta1oMjDnxMgKIgTICiIEyAoiBMgKIgTICiIEyAo1krZ3tJWyszUKypE4aReYQp1lXr1wOlNnYZ+6sIfyti5176Z3b6yru2S0rRjWF3WqfJTx7SV0jMp+1J0Qy5Eh+f/i5nVDy5lX2tbZDLLn0dpVmcMTBuEU2v61Xrj0iUZ6y+vZ7f/07//WI757M6nMlY3enXMrKutpdJ0de+k/HtcCoslpUe0alDn8IVHAMBXAuIECAriBAgK4gQICuIECAriBAiKtVLOrC3J2NQULJoW+XR4fym/PaWUPtNZ/tRb06sYvvPd12VsU6xYmQrbIKWUGtOB+EAPS72O/s6taAdJ0mlNP5FKH6sqTcq+MN9i0RukbeZcaWFC66vayrp4Pt/r5YOPzsgxt29rK8X1PKmM9dGa+6+urVXtwfUQC39OgKAgToCgIE6AoCBOgKAgToCgIE6AoFgr5dzxVRmrG13caaeTTykP17SV8vzGhoydf10X5HryyWdkbDLNr46pKmMPyIgPNqIwVUopta1O2XeELVKZ72bh7BJzkvNaH4rGWAfufvQ7+n6sDvIrRS48o5/z9Y8/lrFbW3sy1nbMqpRCr0pRK0xK81xacz/kOXzhEQDwlYA4AYKCOAGCgjgBgoI4AYJis7XHV3RH6elEDz0Y5gsMDV7Rk9SfNpnhi+dOyFjPfF/Kbv4cuybZ2dWJxGSSjLauT6fQmTqV4HMlZ1ydo3kzhm0SE99NraipCbbmPKqkb+TSYr6W1KvfeFGOGZtU87/+/F0Zu7+rW0aU5gFUcgGBHkMNIYDHCMQJEBTECRAUxAkQFMQJEBTECRAUa6W0M13YZ2Q6Hi9285p/+YKevPzEhp6EvFjqOjClmcReKQvDzEEuzeRw44jY1Hth9qlKFjXlfBPYZ7X+3tau7lOd3+fhRE9uPzDdzY/Gelzd6tfuaJY/x9q0Rzjz1FkZO7ZxQ8Ye7t2UMfnupJQK1ULD1R0yNouCPydAUBAnQFAQJ0BQECdAUBAnQFAQJ0BQCltXBgC+NvhzAgQFcQIEBXECBAVxAgQFcQIEBXECBOV/AbGSPY6gdR9JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X.reshape(32, 32, 3))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_recon.numpy().reshape(32, 32, 3))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check outliers on original CIFAR images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "X = X_train[:500]\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['instance_score', 'feature_score', 'is_outlier']\n"
     ]
    }
   ],
   "source": [
    "od_preds = od.predict(X,\n",
    "                      outlier_type='instance',    # use 'feature' or 'instance' level\n",
    "                      return_feature_score=True,  # scores used to determine outliers\n",
    "                      return_instance_score=True)\n",
    "print(list(od_preds['data'].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot instance level outlier scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = np.zeros(X.shape[0],).astype(int)  # all normal CIFAR10 training instances\n",
    "labels = ['normal', 'outlier']\n",
    "plot_instance_score(od_preds, target, labels, od.threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_recon = od.ae(X).numpy()\n",
    "plot_feature_outlier_image(od_preds, \n",
    "                           X, \n",
    "                           X_recon=X_recon,\n",
    "                           instance_ids=[8, 60, 100, 330],  # pass a list with indices of instances to display\n",
    "                           max_instances=5,  # max nb of instances to display\n",
    "                           outliers_only=False)  # only show outlier predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict outliers on perturbed CIFAR images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perturb CIFAR images by adding random noise to patches (masks) of the image. For each mask size in `n_mask_sizes`, sample `n_masks` and apply those to each of the `n_imgs` images. Then we predict outliers on the masked instances: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb of predictions per image: n_masks * n_mask_sizes \n",
    "n_mask_sizes = 10\n",
    "n_masks = 20\n",
    "n_imgs = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define masks and get images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 2), (4, 4), (6, 6), (8, 8), (10, 10), (12, 12), (14, 14), (16, 16), (18, 18), (20, 20)]\n",
      "(50, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "mask_sizes = [(2*n,2*n) for n in range(1,n_mask_sizes+1)]\n",
    "print(mask_sizes)\n",
    "img_ids = np.arange(n_imgs)\n",
    "X_orig = X[img_ids].reshape(img_ids.shape[0], 32, 32, 3)\n",
    "print(X_orig.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate instance level outlier scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:17<00:00,  2.90it/s]\n"
     ]
    }
   ],
   "source": [
    "all_img_scores = []\n",
    "for i in tqdm(range(X_orig.shape[0])):\n",
    "    img_scores = np.zeros((len(mask_sizes),))\n",
    "    for j, mask_size in enumerate(mask_sizes):\n",
    "        # create masked instances\n",
    "        X_mask, mask = apply_mask(X_orig[i].reshape(1, 32, 32, 3),\n",
    "                                  mask_size=mask_size,\n",
    "                                  n_masks=n_masks,\n",
    "                                  channels=[0,1,2],\n",
    "                                  mask_type='normal',\n",
    "                                  noise_distr=(0,1),\n",
    "                                  clip_rng=(0,1))\n",
    "        # predict outliers\n",
    "        od_preds_mask = od.predict(X_mask)\n",
    "        score = od_preds_mask['data']['instance_score']\n",
    "        # store average score over `n_masks` for a given mask size\n",
    "        img_scores[j] = np.mean(score)\n",
    "    all_img_scores.append(img_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize outlier scores vs. mask sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_plt = [mask[0] for mask in mask_sizes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ais in all_img_scores:\n",
    "    plt.plot(x_plt, ais)\n",
    "    plt.xticks(x_plt)\n",
    "plt.title('Outlier Score All Images for Increasing Mask Size')\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ais_np = np.zeros((len(all_img_scores), all_img_scores[0].shape[0]))\n",
    "for i, ais in enumerate(all_img_scores):\n",
    "    ais_np[i, :] = ais\n",
    "ais_mean = np.mean(ais_np, axis=0)\n",
    "plt.title('Mean Outlier Score All Images for Increasing Mask Size')\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier score')\n",
    "plt.plot(x_plt, ais_mean)\n",
    "plt.xticks(x_plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigate instance level outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 8  # index of instance to look at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_plt, all_img_scores[i])\n",
    "plt.xticks(x_plt)\n",
    "plt.title('Outlier Scores Image {} for Increasing Mask Size'.format(i))\n",
    "plt.xlabel('Mask size')\n",
    "plt.ylabel('Outlier score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstruction of masked images and outlier scores per channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_X_mask = []\n",
    "X_i = X_orig[i].reshape(1, 32, 32, 3)\n",
    "all_X_mask.append(X_i)\n",
    "# apply masks\n",
    "for j, mask_size in enumerate(mask_sizes):\n",
    "    # create masked instances\n",
    "    X_mask, mask = apply_mask(X_i,\n",
    "                              mask_size=mask_size,\n",
    "                              n_masks=1,  # just 1 for visualization purposes\n",
    "                              channels=[0,1,2],\n",
    "                              mask_type='normal',\n",
    "                              noise_distr=(0,1),\n",
    "                              clip_rng=(0,1))\n",
    "    all_X_mask.append(X_mask)\n",
    "all_X_mask = np.concatenate(all_X_mask, axis=0)\n",
    "all_X_recon = od.ae(all_X_mask).numpy()\n",
    "od_preds = od.predict(all_X_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 55 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_outlier_image(od_preds, \n",
    "                           all_X_mask, \n",
    "                           X_recon=all_X_recon, \n",
    "                           max_instances=all_X_mask.shape[0], \n",
    "                           n_channels=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict outliers on a subset of features\n",
    "\n",
    "The sensitivity of the outlier detector can not only be controlled via the `threshold`, but also by selecting the percentage of the features used for the instance level outlier score computation. For instance, we might want to flag outliers if 40% of the features (pixels for images) have an average outlier score above the threshold. This is possible via the `outlier_perc` argument in the `predict` function. It specifies the percentage of the features that are used for outlier detection, sorted in descending outlier score order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_list = [20, 40, 60, 80, 100]\n",
    "\n",
    "all_perc_scores = []\n",
    "for perc in perc_list:\n",
    "    od_preds_perc = od.predict(all_X_mask, outlier_perc=perc)\n",
    "    iscore = od_preds_perc['data']['instance_score']\n",
    "    all_perc_scores.append(iscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize outlier scores vs. mask sizes and percentage of features used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plt = [0] + x_plt\n",
    "for aps in all_perc_scores:\n",
    "    plt.plot(x_plt, aps)\n",
    "    plt.xticks(x_plt)\n",
    "plt.legend(perc_list)\n",
    "plt.title('Outlier Score for Increasing Mask Size and Different Feature Subsets')\n",
    "plt.xlabel('Mask Size')\n",
    "plt.ylabel('Outlier Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer outlier threshold value\n",
    "\n",
    "Finding good threshold values can be tricky since they are typically not easy to interpret. The `infer_threshold` method helps finding a sensible value. We need to pass a batch of instances `X` and specify what percentage of those we consider to be normal via `threshold_perc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current threshold: 0.015\n",
      "New threshold: 0.0017021061840932791\n"
     ]
    }
   ],
   "source": [
    "print('Current threshold: {}'.format(od.threshold))\n",
    "od.infer_threshold(X, threshold_perc=99)  # assume 1% of the training data are outliers\n",
    "print('New threshold: {}'.format(od.threshold))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
